Role of Different Fuzzy Min- Max Neural Network for Pattern Classification

Jaitra Chakraborti

Abstract: Different neural networks related to Fuzzy min-max (FMM) has been studied and amongst all, Enhanced Fuzzy min-max (EFMM) neural network is most recent. For classification of patterns a new Enhanced Fuzzy Min-Max (EFMM) algorithm has been studied. The aim of EFMM is to improve the performance and minimize the restrictions that are possessed by original fuzzy min-max (FMM) network. Three heuristic rules are used to improve the learning algorithm of FMM. First, to eliminate the problem of overlapping during hyperbox expansion, new overlapping rules has been suggested. Second, to discover other overlapping cases the hyperbox test rule has been extended. Third, to resolve the hyperbox overlapping cases, hyperbox contraction rule is provided.

Keywords: Fuzzy minmax FMM model, hyperbox structure, neural network learning, online learning, pattern classification

How to Cite?: Jaitra Chakraborti, "Role of Different Fuzzy Min- Max Neural Network for Pattern Classification", Volume 3 Issue 12, December 2014, International Journal of Science and Research (IJSR), Pages: 1343-1346, https://www.ijsr.net/getabstract.php?paperid=SUB14624, DOI: https://dx.doi.org/10.21275/SUB14624